An ANFIS-Based Fault Classification Approach in Double-Circuit Transmission Line Using Current Samples

Transmission line protective relaying is an essential feature of a reliable power system operation. Fast detecting, isolating, locating and repairing of the different faults are critical in maintaining a reliable power system operation. On the other hand, classification of the different fault types plays very significant role in digital distance protection of the transmission line. Accurate and fast fault classification can prevent from more damages in the power system. In this paper, an approach is presented to classify the fault in a double-circuit transmission line based on the adaptive Neuro-Fuzzy Inference System (ANFIS) using three phase current samples of only one terminal. This method is independent of effects of variation of fault inception angle, fault location, fault resistance and load angle. MATLAB/Simulink is used to produce fault signals. The proposed method is tested by simulating different scenarios on a given transmission line model. The simulation results denote that the proposed approach for fault identification is able to classify all the faults on the parallel transmission line within half cycle after the inception of fault.